Resource distribution method for throughput maximization in cooperative cognitive SIMO network

ABSTRACT

A resource distribution method for throughput maximization in a cooperative cognitive SIMO network. The primary user sends data after receiving a cooperation confirmation message, and the cognitive network keeps silent, receives the information data of the primary user and simultaneously decodes the data. The cognitive users which successfully decode the data send the data of themselves to the cognitive base station and forward the data of the primary user; the cognitive users which cannot successfully decode the data only send the data of themselves and do not forward the data of the primary user. The cognitive base station eliminates the interference of the data of primary user and performs beamforming for the signals. According to the combined adjustment the maximum throughput performance can be realized in the cognitive network.

CROSS REFERENCE TO RELATED PATENT APPLICATION

The present application is the US national stage of PCT/CN2011/076732filed on Jun. 30, 2011, which claims the priority of the Chinese patentapplication No. 201110180262.7 filed on Jun. 29, 2011, which applicationis incorporated herein by reference.

FIELD OF THE INVENTION

The invention belongs to the technical field of cognitive radio andspecifically relates to a new resource distribution method forthroughput maximization in a cooperative relay-based cognitivesingle-input multiple-output (referred to as SIMO) network.

BACKGROUND OF THE INVENTION

Cognitive radio is one of the most popular wireless techniques. Theemergence of the cognitive radio has changed the way of exclusivelyusing spectrum resources by a licensed user. Each cognitive user canperform interactive perception with the wireless communicationenvironment and automatically changes its transmit-receive parameters,so that the authorized spectrum can be dynamically reused on the premiseof ensuring the normal communication of the licensed user (primary user)and the utilization efficiency of the spectrum can be significantlyimproved. In addition, a multi-antenna way is recognized as one of therequired technical schemes for further high-speed wireless networks. Amulti-antenna communication system increases space dimension on thebasis of original frequency domain, time domain and code domain, withthe advanced space-time signal processing technologies, its capacity canbe upgraded by many times without increasing bandwidth and transmissionpower, in addition, the anti-interference and anti-fading performancesof the communication system can be simultaneously enhanced, so that thescarce spectrum can be effectively eased and high-speed businessdevelopment can be further provided. The combination of themulti-antenna technique and the cognitive radio technique can becombined to realize broad application.

At present, there are two well-known spectrum access models forcognitive radio, namely the spectrum hole-based opportunistic spectrumaccess and the interference temperature-based spectrum sharing.

The spectrum hole-based opportunistic spectrum access is as follows:spectrum resources which are not used by the primary user in specificpositions at specific times are called as spectrum holes, and theopportunistic spectrum access manner utilizes the spectrum holes tocommunicate, which is the direct realization of cognitive radio. Thiskind of access way does not need to control the transmission power, buta high-precision spectrum detection technique is required in thecognitive network; and when the traffic in primary network is busy, itis very difficulty to obtain the communication opportunity in theopportunistic spectrum access model.

The interference temperature-based spectrum sharing is as follows:interference temperature is defined at the front end of radio frequencyof a wireless receiver, which is used for measuring the interferencereceived by a receiver in a certain geographical position within acertain frequency band, and the maximum interference temperature whichcan be tolerated by normal communication of the receiver is called as aninterference temperature boundary. As long as the interference from thecognitive network to the primary receiver can be controlled within theinterference temperature boundary, the licensed frequency band can bereused by the cognitive user without affecting the normal communicationof the primary user. By utilizing this kind of access way, the cognitivenetwork and the primary user can simultaneously use the same licensedfrequency band in the same position, but the transmission power of thecognitive network must be controlled to meet the interferencetemperature boundary of the primary user, so that the spectrum sharingway can not realize a large-range network coverage and the communicationperformance is very poor when the distance to the primary network isshort.

At present, in order to overcome the problems of difficult access andpoor communication in the existing cognitive radio access methods, theapplicant provides an application number of CN201110178680.2 named“Cognitive SIMO Network Access Method Based on Cooperative Relay”, whichproposes a new access method for the uplink of a multi-antenna cognitivenetwork. The access method allows the cognitive network and the primaryuser to simultaneously use the same licensed spectrum in the samegeographical position, furthermore, a large-range network coverage canbe realized and great network throughput can be obtained when thedistance to the primary network is short, which overcomes thedeficiencies in the existing cognitive radio access methods. However,for the access method, how to realize high-efficient resourcedistribution, to maximize the throughput of the cognitive SIMO networkon the premise of ensuring the target transmission rate of the primaryuser is a problem needed to be solved urgently.

SUMMARY OF THE INVENTION

The invention aims to provide a resource distribution method forthroughput maximization in cooperative cognitive SIMO network on thepremise of ensuring the target transmission rate of a primary user.

The invention is realized through the following technical scheme:

A resource distribution method for throughput maximization in acooperative cognitive SIMO network comprises the following steps:

Step 1: the transmitter of the primary user broadcasts a cooperationrequest message (referred to as CRM), the receiver of the primary userreplies a cooperation acknowledge message (referred to as CAM). Thecognitive base station estimates the channel state information in thenetwork from the received CRM and CAM, then judges whether the cognitiveSIMO network has the ability of cooperating with the primary user toachieve the target transmission rate of the primary user or not, if yes,the cognitive base station sends a cooperation confirmation message(referred to as CCM) to the primary user and the cognitive SIMO networkis accessible to the frequency band licensed to the primary user; and ifno, the cognitive SIMO network is non-accessible to the licensedfrequency band;

Step 2: the cognitive network receives the data of the primary user,that comprises:

-   -   after the transmitter and the receiver of the primary user        receive the CCM, the transmitter of the primary user starts to        send the information data, the cognitive network keeps silent        and receives data of the primary user, simultaneously, the        cognitive base station and the cognitive users being capable of        successfully decoding the data of the primary user decode the        data;

Step 3: the cognitive users send data of themselves and simultaneouslyrelay the data of the primary user, that comprises:

-   -   the cognitive users which successfully decode the data of the        primary user use part of their transmission power α_(c)p_(c),        c∈U₁ to send the data of themselves to the cognitive base        station, and the remaining transmission power (1−α_(c))p_(c),        c∈U₁ is used for forwarding the data of the primary user to the        primary receiver; the cognitive users which can not successfully        decode the data do not forward the data of the primary user, all        their transmission power p_(c), c∈U₂ is used for sending the        data of themselves to the cognitive base station, Wherein the        set U₁ represents the set of the cognitive users being capable        of successfully decoding the data of the primary user, the set        U₂ represents the set of the cognitive users being incapable of        successfully decoding the data of the primary user, p_(c)        represents the transmission power of the cth cognitive user and        α_(c) represents the power distribution factor of the cth        cognitive user;

Step 4: the cognitive base station eliminates the interference caused bythe data of the primary user from the received mixed signals andperforms beamforming for the signals after eliminating the interference.According to the combined adjustment of the transmission power vectorp=[p₁, p₂, . . . , p_(N)]^(T), the power distribution factor vectorα=[α₁, α₂, . . . , α_(N)]^(T), α_(c)=1, c∈U₂, and the beamforming weightvectors w_(c)=[w_(c,1), w_(c,2), . . . , w_(c,M)]^(T), c=1, 2, . . . ,N, the maximum throughput can be realized in the cognitive SIMO networkon the premise of ensuring the target transmission rate of the primaryuser. Wherein M represents the number of antennas configured at thecognitive base station, and N represents the number of the cognitiveusers in the cognitive network.

The specific implementation method of the step 4 in the invention is asfollows:

-   -   4.1 Initializing: n=0, p_(c) ^((n))=p_(c,max), c=1, 2, . . . ,        N, α_(c) ^((n))=x, c=1, 2, . . . , N and R_(sum) ^((n))=0,        wherein n represents the number of iterations, p_(c) ^((n)) and        α_(c) ^((n)) represent the transmission power and the power        distribution factor of the cth cognitive user at the nth        iteration respectively, p_(c,max) represents the peak        transmission power allowed by the cth cognitive user, and        R_(sum) ^((n)) represents the throughput of the cognitive        network at the nth iteration; and setting a judging criterion of        iteration stopping ε, ε∈[10⁻², 10⁻⁴];    -   4.2 Adding 1 to the number of the iterations: n=n+1;    -   4.3 Fixing the transmission power vector and the power        distribution factor vector as p^((n-1)) and α^((n-1)) of the        (n−1)th iteration, and enabling the cognitive base station to        utilize a maximum SINR beamforming criterion to calculate the        beamforming weight vectors w_(c) ^((n)), c=1, 2, . . . , N of        the nth iteration, wherein the calculation formula is as        follows:

$\begin{matrix}{w_{c}^{(n)} = {{\eta\left( {{\sum\limits_{{i = 1},{i \neq c}}^{N}\;{\alpha_{i}^{({n - 1})}p_{i}^{({n - 1})}h_{i}^{cbH}h_{i}^{cb}}} + {\sigma_{b}^{2}I_{M}}} \right)}^{- 1}h_{c}^{cb}}} & (1)\end{matrix}$

-   -    Wherein I_(M) represents an M×M unit matrix, η is a scaler        factor for normalizing w_(c) ^((n)), and h_(c) ^(cb), c=1, 2, .        . . , N represents an M-dimensional channel vector response from        the cth cognitive user to the cognitive base station, h_(i)        ^(cbH) is a conjugate of h_(i) ^(cb), and σ_(b) ² represents the        channel noise power received by the cognitive base station;    -   4.4 Fixing the beamforming weight vectors as w_(c) ^((n)), c=1,        2, . . . , N of the nth iteration, fixing the power distribution        factor vector as α^((n-1)) of the (n−1)th iteration and        calculating the transmission power vector p^((n)) of the nth        iteration;    -   4.5 Fixing the beamforming weight vectors and the transmission        power vector as w_(c) ^((n)), c=1, 2, . . . , N and p^((n)) of        the nth iteration and calculating the power distribution factor        vector α^((n)) of the nth iteration;    -   4.6 Calculating the throughput R_(sum) ^((n)) of the cognitive        network after the nth iteration by utilizing the following        formula:

$\begin{matrix}{R_{sum}^{(n)} = {\sum\limits_{c = 1}^{N}\;{\frac{1}{2}{\log\left( {1 + \frac{\alpha_{c}^{(n)}p_{c}^{(n)}{{w_{c}^{{(n)}H}h_{c}^{cb}}}^{2}}{{\sum\limits_{{i = 1},{i \neq c}}^{N}\;{\alpha_{i}^{(n)}p_{i}^{(n)}{{w_{c}^{{(n)}H}h_{i}^{cb}}}^{2}}} + \sigma_{b}^{2}}} \right)}}}} & (2)\end{matrix}$

-   -   4.7 Judging whether the iteration stopping condition |R_(sum)        ^((n))−R_(sum) ^((n-1))|/R_(sum) ^((n-1))≦ε is met or not, if        so, indicating that the throughputs of the cognitive network,        which are obtained in the nth iteration and the (n−1)th        iteration hardly change, namely the iteration process converges,        then continuously implementing the step 4.8; and otherwise,        repeatedly implementing the step 4.2;    -   4.8 Outputting the final values after convergence: w_(c)=w_(c)        ^((n)), c=1, 2, . . . , N, p=p^((n)), α=α^((n)) and        R_(sum)=R_(sum) ^((n)), wherein the throughput R_(sum) of the        cognitive network is maximum at this time.

At the nth iteration, the calculation process of the transmission powervector p^((n)) in the step 4.4 is as follows:

-   -   1) Initializing: m=0 and {circumflex over (p)}_(c) ^((m))=0,        c=1, 2, . . . , N, wherein m represents the number of        iterations, {circumflex over (p)}_(c) ^((m)) represents the        transmission power of the cth cognitive user at the mth        iteration, and setting the judging criteria of iteration        stopping ε, ε∈[10⁻², 10⁻⁴];    -   2) Adding 1 to the number of iterations: m=m+1;    -   3) Calculating the transmission power {circumflex over (p)}_(c)        ^((m)), c=1, 2, . . . , N of the cth cognitive user at the mth        iteration by utilizing the following formula;

$\begin{matrix}{{\overset{\bullet}{p}}_{\; c}^{(m)} = \left\lbrack \left( {\sum\limits_{{k = 1},{k \neq c}}^{N}\;\frac{\alpha_{c}^{({n - 1})}{{w_{k}^{{(n)}H}h_{c}^{cb}}}^{2}}{{\sum\limits_{{i = 1},{i \neq k}}^{N}\;{{\overset{\bullet}{p}}_{i}^{({m - 1})}\alpha_{i}^{({n - 1})}{{w_{k}^{{(n)}H}h_{c}^{cb}}}^{2}}} + \sigma_{b}^{2}}} \right)^{- 1} \right\rbrack^{p_{c,\max}}} & (3)\end{matrix}$

-   -    Wherein [b]^(p) ^(c,max) takes a minimum value of b and        p_(c,max);    -   4) Judging whether the iteration stopping condition ∥{circumflex        over (p)}^((m))−{circumflex over (p)}^((m-1))∥/∥{circumflex over        (p)}^((m-1))∥<ε is met or not, if so, indicating that the power        vectors obtained in the nth iteration and the (n−1)th iteration        hardly change, namely the iteration process converges, then        continuously implementing the step 5); and otherwise, repeatedly        implementing the step 2);    -   5) Outputting the final convergent value: p^((n))={circumflex        over (p)}^((m)).

At the nth iteration, the calculation process of the power distributionfactor vector α^((n)) in the step 4.5 is as follows:

-   -   1) Initializing: m=0, {circumflex over (α)}_(c) ^((m))=0(c∈U₁)        and {circumflex over (α)}_(c) ^((m))=1(c∈U₂), wherein m        represents the number of iterations, {circumflex over (α)}_(c)        ^((m)) represents the power distribution factor of the cth        cognitive user at the mth iteration; and setting the judging        criteria of iteration stopping ε, ε∈[10⁻², 10⁻⁴];    -   2) Adding 1 to the number of iterations: m=m+1;    -   3) Calculating the power distribution factor of the c(c∈U₁)th        cognitive user at the mth iteration by utilizing the following        formula:

$\begin{matrix}{{{\overset{\bullet}{\alpha}}_{c}^{(m)} = \left\lbrack \left( {{\sum\limits_{{k \in U_{1}},{k \neq c}}\;\frac{p_{c}^{(n)}{{w_{k}^{{(n)}H}h_{c}^{cb}}}^{2}}{{\sum\limits_{{i \in U_{1}},{i \neq k}}\;{{\overset{\bullet}{\alpha}}_{i}^{({m - 1})}p_{i}^{(n)}{{w_{k}^{{(n)}H}h_{i}^{cb}}}^{2}}} + q_{k}^{1}}} + \mspace{104mu}{\sum\limits_{k \in U_{2}}\;\frac{p_{c}^{(n)}{{w_{k}^{{(n)}H}h_{c}^{cb}}}^{2}}{{\sum\limits_{i \in U_{1}}\;{\alpha_{i}^{({m - 1})}{\overset{\bullet}{p}}_{i}{{w_{k}^{{(n)}H}h_{i}^{cb}}}^{2}}} + q_{k}^{2}}} + {\lambda\;{g_{c}\left( p_{c}^{(n)} \right)}}} \right)^{- 1} \right\rbrack^{1}}{Wherein}{{q_{k}^{1} = {{\sum\limits_{i \in U_{2}}\;{p_{i}^{(n)}{{w_{k}^{{(n)}H}h_{i}^{cb}}}^{2}}} + {\sigma_{b}^{2}\left( {k \in U_{1}} \right)}}},{q_{k}^{2} = {{{\sum\limits_{{i \in U_{2}},{i \neq k}}\;{p_{i}^{(n)}{{w_{k}^{{(n)}H}h_{i}^{cb}}}^{2}}} + {{\sigma_{b}^{2}\left( {k \in U_{2}} \right)}{and}{g_{c}\left( p_{c}^{(n)} \right)}}} = {\left\lbrack {1 + {1/\left( {2^{2R_{p\; k}} - 1} \right)}} \right\rbrack p_{c}^{(n)}{h_{c}^{cp}}^{2}}}},}} & (4)\end{matrix}$

-   -    λ represents an any auxiliary Lagrange factor and h_(c) ^(cp)        represents the channel response from the cth cognitive user to        the receiver of the primary user. For the cognitive users in the        set U₂, the power distribution factor {circumflex over (α)}_(c)        ^((m))=1(c∈U₂);    -   4) Judging whether the iteration stopping condition ∥{circumflex        over (α)}^((m))−{circumflex over (α)}^((m-1))∥/∥{circumflex over        (α)}^((m-1))∥<ε is met or not, if so, indicating that the power        distribution factor vectors obtained in the nth iteration and        the (n−1)th iteration hardly change, namely the iteration        process converges, then continuously implementing the step 5);        and otherwise, repeatedly implementing the step 2);    -   5) Outputting the final convergent value: α(λ)={circumflex over        (α)}^((m)).

The value of the auxiliary Lagrange factor λ of the invention isdetermined according to a bisection search algorithm, and the specificimplementation process is as follows:

-   -   1) Setting initial values λ⁺=0 and λ⁻ of a bisection search        algorithm, wherein λ⁺ is the initial value for realizing

${{\sum\limits_{c \in U_{1}}\;{{{\overset{\_}{\alpha}}_{c}\left( \lambda^{+} \right)}{g_{c}\left( p_{c}^{(n)} \right)}}} < {C\left( p^{(n)} \right)}},$

-   -    indicating that λ⁺ can enable the cognitive network to provide        a greater transmission rate than the target transmission rate of        the primary user; and λ⁻ is the initial value for realizing

${{\sum\limits_{c \in U_{1}}\;{{{\overset{\_}{\alpha}}_{c}\left( \lambda^{+} \right)}{g_{c}\left( p_{c}^{(n)} \right)}}} > {C\left( p^{(n)} \right)}},$

-   -    indicating that λ⁻ can enable the cognitive network to provide        a less transmission rate than the target transmission rate of        the primary user. Wherein

${{C\left( p^{(n)} \right)} = {{\frac{1}{2^{2R_{p\; k}} - 1}{\sum\limits_{c \in N_{1}}\;{p_{c}^{(n)}{h_{c}^{cp}}^{2}}}} - {\sum\limits_{c \in N_{2}}\;{p_{c}^{(n)}{h_{c}^{cp}}^{2}}} - \sigma_{p}^{2}}},\sigma_{p}^{2}$

-   -    is the channel noise power received by the receiver of the        primary user, and R_(pk) is the target transmission rate of the        primary user. Setting the judging criteria of iteration stopping        ε, ε∈[10⁻², 10⁻⁴];    -   2) Enabling

${\lambda = \frac{\lambda^{+} + \lambda^{-}}{2}},$

-   -    and obtaining the power distribution factor vector α(λ)        according to the above iterative calculation process of the        power distribution factor;

${{{If}\mspace{14mu}{\sum\limits_{c \in N_{1}}\;{{{\overset{\_}{\alpha}}_{c}(\lambda)}{g_{c}\left( p_{c}^{(n)} \right)}}}} < {C\left( p^{(n)} \right)}},{{{{then}\mspace{14mu}\lambda^{-}} = \lambda};}$${{{If}\mspace{14mu}{\sum\limits_{c \in N_{1}}\;{{{\overset{\_}{\alpha}}_{c}(\lambda)}{g_{c}\left( p_{c}^{(n)} \right)}}}} > {C\left( p^{(n)} \right)}},{{{{then}\mspace{14mu}\lambda^{+}} = \lambda};}$

-   -   3) Judging whether the search stopping condition

${{{{\sum\limits_{c \in U_{1}}\;{{{\overset{\_}{\alpha}}_{c}(\lambda)}{g_{c}\left( p_{c}^{(n)} \right)}}} - {C\left( p^{(n)} \right)}}}/{C\left( p^{(n)} \right)}} \leq ɛ$

-   -    is met or not, if so, indicating that the transmission rate        which is provided by the cognitive network for the primary user        is basically equivalent to the target transmission rate of the        primary user, then continuously implementing the step 4); and        otherwise, repeatedly implementing the step 2);    -   4) Outputting the final result: α^((n))= α(λ).

In the step 4.1 of the invention, α_(c) ^((n))=x, c=1, 2, . . . , N,wherein x is a decimal and x∈[0, 1].

Compared with the prior art, the invention has the following advantages:

-   -   The invention designs the resource distribution method for        throughput maximization in the cognitive SIMO network based on        the cooperative relay. According to the combined adjustment of        the transmission power vector, the power distribution factor        vector and the beamforming weight vectors, the maximum        throughput performance of the cognitive network can be realized        while the target transmission rate of the primary user is        ensured, in addition, very fast convergence rate can be realized        in the proposed resource distribution method.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a cognitive SIMO network access model based on cooperativerelay.

-   -   (a) A cognitive SIMO network access model; and    -   (b) A resource distribution model after the access of the        cognitive SIMO network.

FIG. 2 is a change curve of the data transmission rate provided by thecognitive network for the primary user along with the peak transmissionpower of the cognitive users according to the resource distributionmethod of the invention.

FIG. 3 is a change curve of the achievable network throughput along withthe distance between the cognitive network and the primary networkaccording to the resource distribution method of the invention.

DETAILED DESCRIPTION OF THE INVENTION

An embodiment of the invention is described as follows: systemsimulation adopts MatLab simulation, and the setting of parameters doesnot affect generality. A primary user system comprises a single-antennatransmitter and a single-antenna receiver, and the receiver is randomlydistributed on a circumference with a radius of 200 m by taking thetransmitter as the center of a circle. A cognitive network comprises amulti-antenna cognitive base station and three single-antenna cognitiveusers, and the three cognitive users are randomly distributed on thecircumference with the radius of 200 m by taking the base station as thecenter of the circle. A logarithmic path loss model is used for modelinglarge-scale path loss of the channels, and a loss factor is set as 4; aRayleigh fading model with mean 1 is used for modeling small-scalefading of the channels; the noise power at the receiver is set as σ_(b)²=σ_(c) ²=σ_(p) ²=−110 dBm; the transmission power of the primarytransmitter is 0 dBm; and the three cognitive users have a same peaktransmission power p_(max).

The specific process of the embodiment is described by taking afollowing independent random test as an example. In the random test, thenumber of antennas of the cognitive base station is set as M=3, thedistance between the cognitive base station and the transmitter of theprimary user is set as 100 m, the allowable peak transmission power ofthe three cognitive users is set as p_(max)=20 dBm, and the targettransmission rate of the primary user is set as R_(pk)=1 bps/Hz.

Step 1: the transmitter of the primary user broadcasts a cooperationrequest message CRM, the receiver of the primary user replies acooperation acknowledge message CAM. The cognitive base stationestimates the channel state information in the network from the receivedCRM and CAM, then judges whether the cognitive SIMO network has theability of cooperating with the primary user to achieve the targettransmission rate of the primary user or not, if yes, the cognitive basestation sends a cooperation confirmation message CCM to the primary userand the cognitive SIMO network is accessible to the frequency bandlicensed to the primary user; and otherwise, the cognitive SIMO networkis non-accessible to the licensed frequency band. In the random test,according to the assumed path loss and path fading models, four groupsof the channel responses in the network which are randomly generated byMatlab simulation software are as follows:

-   -   1. The channel response vector from the transmitter of the        primary user to the cognitive base station: h^(pb)=[1.1967×10⁻⁴,        1.117×10⁻⁵, 1.1488×10⁻⁴]^(T);    -   2. The channel responses from the transmitter of the primary        user to all the cognitive users: h₁ ^(pc)=2.9093×10⁻⁵, h₂        ^(pc)=2.6325×10⁻⁵, h₃ ^(pc)=1.7732×10⁻⁵;    -   3. The channel response vectors from all the cognitive users to        the cognitive base station: h₁ ^(cb)=[3.8387×10⁻⁵, 2.0909×10⁻⁵,        3.1236×10⁻⁵]^(T), h₂ ^(cb)=[1.545×10⁻⁵, 1.9064×10⁻⁵,        9.1956×10⁻⁷]^(T), h₃ ^(cb)=[5.619×10⁻⁶, 3.8369×10⁻⁵,        1.7549×10⁻⁵]^(T);    -   4. The channel responses from all the cognitive users to the        receiver of the primary user: h₁ ^(cp)=1.535×10⁻⁵, h₂        ^(cp)=1.8538×10⁻⁵, h₃ ^(cp)=1.167×10⁻⁵.

According to the cognitive SIMO network access method based oncooperative relay in the patent number of 201110178680.2, which isprovided by the applicant, we can judge that the cognitive SIMO networkis accessible to the frequency band licensed to the primary user, andall the three cognitive users and the cognitive base station cansuccessfully decode the data of the primary user, so that the cognitivebase station sends the CCM to the primary user.

Step 2: the cognitive network receives the data of the primary user,that comprises:

-   -   after the transmitter and the receiver of the primary user        receive the CCM, the primary transmitter starts to send its        information data, the cognitive network keeps silent and        receives the data of the primary user, simultaneously, the        cognitive base station and the cognitive users being capable of        successfully decoding the data of the primary user decode the        data. In the random test, both the cognitive base station and        the three cognitive users decode the data of the primary user.

Step 3: the cognitive users send the data of themselves andsimultaneously relay the data of the primary user, that comprises:

-   -   the cognitive user 1, the cognitive user 2 and the cognitive        user 3 which successfully decode the data of the primary user in        the step 2 use part of their transmission power α₁p₁, α₂p₂ and        α₃p₃ to send the data of themselves to the cognitive base        station, and the remaining transmission power (1−α₁)p₁, (1−α₂)        p₂ and (1−α₃) p₃ are used for forwarding the data of the primary        user to the primary receiver;

Step 4: the cognitive base station eliminates the interference caused bythe data of the primary user from the received mixed signals andperforms beamforming on the signals after eliminating the interference.According to the combined adjustment of the transmission power vectorp=[p₁, p₂, p₃]^(T), the power distribution factor vector α=[α₁, α₂,α₃]^(T), and the beamforming weight vectors w₁, w₂, w₃, the maximumthroughput can be realized in the cognitive SIMO network on the premiseof ensuring the target transmission rate R_(pk) of the primary user.

In the step 4, p, α and w₁, w₂, w₃ are determined according to thefollowing iteration process, that comprises:

-   -   Step 4.1 Initializing: n=0, p_(c) ^((n))=20 dBm (c=1, 2, 3),        α_(c) ^((n))=1(c=1, 2, 3) and R_(sum) ^((n))=0, wherein n        represents the number of iterations, p_(c) ^((n)) and α_(c)        ^((n)) represent the transmission power and the power        distribution factor of the cth cognitive user at the nth        iteration respectively, and R_(sum) ^((n)) represents the        throughput of the cognitive network at the nth iteration.        Setting the judging criteria of iteration stopping ε=0.001.    -   Step 4.2 Adding 1 to the number of iterations: n=n+1.    -   Step 4.3 Fixing the transmission power vector and the power        distribution factor vector as p^((n-1)) and α^((n-1)) of the        (n−1)th iteration and calculating the beamforming weight vectors        w_(c) ^((n)) (c=1, 2, 3) at the nth iteration:

$\begin{matrix}{w_{c}^{(n)} = {{\eta\left( {{\sum\limits_{{i = 1},{i \neq c}}^{3}\;{\alpha_{i}^{({n - 1})}p_{i}^{({n - 1})}h_{i}^{cbH}h_{i}^{c\; b}}} + {\sigma_{b}^{2}I_{N_{t}}}} \right)}^{- 1}h_{c}^{cb}}} & (5)\end{matrix}$

-   -    Wherein I_(N) _(t) represents an N_(t)×N_(t) unit matrix and η        is a scaler factor for normalizing w_(c) ^((n)).    -   Step 4.4 Fixing the beamforming weight vectors as w_(c) ^((n))        (c=1, 2, 3) of the nth iteration, fixing the power distribution        factor vector as α^((n-1)) of the (n−1)th iteration and        calculating the transmission power vector p^((n)) of the nth        iteration. p^((n)) is determined by the following iteration        process:    -   1) Initializing: m=0 and {circumflex over (p)}_(c) ^((m))=0        (c=1, 2, 3), wherein m represents the number of iterations,        {circumflex over (p)}_(c) ^((m)) represents the transmission        power of the cth cognitive user at the mth iteration. Setting        the judging criteria of iteration stopping ε=0.001.    -   2) Adding 1 to the number of iterations: m=m+1    -   3) Calculating the transmission power vector {circumflex over        (p)}_(c) ^((m)) (c=1, 2, 3) at the mth iteration

$\begin{matrix}{{\overset{\bullet}{p}}_{\; c}^{(m)} = \left\lbrack \left( {\sum\limits_{{k = 1},{k \neq c}}^{3}\;\frac{\alpha_{c}^{({n - 1})}{{w_{k}^{{(n)}H}h_{c}^{cb}}}^{2}}{{\sum\limits_{{i = 1},{i \neq k}}^{N}\;{{\overset{\bullet}{p}}_{i}^{({m - 1})}\alpha_{i}^{({n - 1})}{{w_{k}^{{(n)}H}h_{i}^{cb}}}^{2}}} + \sigma_{b}^{2}}} \right)^{- 1} \right\rbrack^{p_{c,\max}}} & (6)\end{matrix}$

-   -   4) Judging whether the stopping condition ∥{circumflex over        (p)}^((m))−{circumflex over (p)}^((m-1))∥/∥{circumflex over        (p)}^((m-1))∥<ε is met or not, if so, continuously implementing        the step 5); and otherwise, repeatedly implementing the step 2);    -   5) Outputting the final convergent value p^((n))={circumflex        over (p)}^((m)), wherein p^((n)) is the transmission power        vector of the nth iteration in the step 4.4.    -   Step 4.5 Fixing the beamforming weight vectors and the        transmission power vector as w_(c) ^((n)) (c=1, 2, 3) and        p^((n)) of the nth iteration, and calculating the power        distribution factor vector α^((n)) of the nth iteration. A        non-negative auxiliary variable, namely a Lagrange factor λ,        needs to be introduced in the calculation process of α^((n)),        the λ is determined through a bisection search algorithm, and        the corresponding α(λ) needs to be calculated during the process        of updating λ every time. For any λ≧0, α(λ) is determined        according to the following iteration process:        -   1) Initializing: m=0 and {circumflex over (α)}_(c) ^((m))=0            (c=1, 2, 3), wherein m represents the number of iterations,            and {circumflex over (α)}_(c) ^((m)), represents the power            distribution factor of the cth cognitive user at the mth            iteration. Setting the judging criteria of iteration            stopping ε=0.001.        -   2) Adding 1 to the number of iterations: m=m+1        -   3) Calculating the power distribution factor of the cth            cognitive user at the mth iteration by utilizing the            following formula:

$\begin{matrix}{{\overset{\bullet}{\alpha}}_{\; c}^{(m)} = \left\lbrack \left( {{\sum\limits_{{k = 1},{k \neq c}}^{3}\;\frac{p_{c}^{(n)}{{w_{k}^{{(n)}H}h_{c}^{cb}}}^{2}}{{\sum\limits_{{i = 1},{i \neq k}}^{3}\;{{\overset{\bullet}{\alpha}}_{i}^{({m - 1})}p_{i}^{(n)}{{w_{k}^{{(n)}H}h_{i}^{cb}}}^{2}}} + \sigma_{b}^{2}}} + {\lambda\;{g_{c}\left( p_{c}^{(n)} \right)}}} \right)^{- 1} \right\rbrack^{1}} & (7)\end{matrix}$

-   -    wherein g_(c)(p_(c) ^((n)))=[1+1/(2^(2R) ^(pk) −1)]p_(c)        ^((n))|h_(c) ^(cp)|².        -   4) Judging whether the stopping condition ∥{circumflex over            (α)}^((m))−{circumflex over (α)}^((m-1))∥/∥{circumflex over            (α)}^((m-1))∥<ε is met or not, if so, continuously            implementing the step 5); and otherwise, repeatedly            implementing the step 2);        -   5) Outputting the final convergent value: α(λ)={circumflex            over (α)}^((m)).

The auxiliary variable λ is determined according to the followingbisection search algorithm:

-   -   1) Setting the initial values λ⁺=0 and λ⁻=10⁷ of the bisection        search algorithm. Wherein λ⁺ represents the initial value for        realizing

${{\sum\limits_{c = 1}^{3}\;{{{\overset{\_}{\alpha}}_{c}\left( \lambda^{+} \right)}{g_{c}\left( p_{c}^{(n)} \right)}}} > {C\left( p^{(n)} \right)}},$

-   -    λ⁻ represents the initial value for realizing

${\sum\limits_{c = 1}^{3}\;{{{\overset{–}{\alpha}}_{c}\left( \lambda^{-} \right)}{g_{c}\left( p_{c}^{(n)} \right)}}} < {C\left( p^{(n)} \right)}$

-   -    and

${C\left( p^{(n)} \right)} = {{\frac{1}{2^{2R_{p\; k}} - 1}{\sum\limits_{c = 1}^{3}\;{p_{c}^{(n)}{h_{c}^{cp}}^{2}}}} - {\sigma_{b}^{2}.}}$

-   -    Setting the judging criteria of iteration stopping ε=0.001.    -   2) Enabling

${\lambda = \frac{\lambda^{+} + \lambda^{-}}{2}},$

-   -    and obtaining the power distribution factor vector α(λ)        according to the above calculation process. If

${{\sum\limits_{c = 1}^{3}{{{\overset{\_}{\alpha}}_{c}(\lambda)}{g_{c}\left( p_{c}^{(n)} \right)}}} < {C\left( p^{(n)} \right)}},$

-   -    then λ⁻=λ; and if

${{\sum\limits_{c = 1}^{3}{{{\overset{\_}{\alpha}}_{c}(\lambda)}{g_{c}\left( p_{c}^{(n)} \right)}}} > {C\left( p^{(n)} \right)}},$

-   -    then λ⁺=λ.    -   3) Judging whether the stopping condition

${{{{\sum\limits_{c = 1}^{3}{{{\overset{\_}{\alpha}}_{c}(\lambda)}{g_{c}\left( p_{c}^{(n)} \right)}}} - {C\left( p^{(n)} \right)}}}/{C\left( p^{(n)} \right)}} \leq ɛ$

-   -    is met or not, if so, continuously implementing the step 4);        and otherwise, repeatedly implementing the step 2);    -   4) Outputting the final result α^((n))= α(λ), wherein α^((n)) is        the power distribution factor vector of the nth iteration in the        step 4.5.    -   Step 4.6 Calculating the throughput R_(sum) ^((n)) of the        cognitive network after the nth iteration by utilizing the        following formula:

$\begin{matrix}{R_{sum}^{(n)} = {\sum\limits_{c = 1}^{3}{\frac{1}{2}{\log\left( {1 + \frac{\alpha_{c}^{(n)}p_{c}^{(n)}{{w_{c}^{{(n)}H}h_{c}^{cb}}}^{2}}{{\sum\limits_{{i = 1},{i \neq c}}^{3}{\alpha_{i}^{(n)}p_{i}^{(n)}{{w_{c}^{{(n)}H}h_{i}^{cb}}}^{2}}} + \sigma^{2}}} \right)}}}} & (8)\end{matrix}$

-   -   Step 4.7 Judging whether the iteration stopping condition        |R_(sum) ^((n))−R_(sum) ^((n-1))|/R_(sum) ^((n-1))≦ε is met or        not, if so, continuously implementing the step 4.8; and        otherwise, repeatedly implementing the step 4.2;    -   Step 4.8 Outputting the final values after convergence:        w_(c)=w_(c) ^((n)) (c=1, 2, 3), p=p^((n)), α=α^((n)) and        R_(sum)=R_(sum) ^((n))

According to the above-mentioned steps, in the first iteration in therandom test, w₁ ⁽¹⁾=[0.7145, 0.3892, 0.5814]^(T), w₂ ⁽¹⁾=[0.6292,0.7764, 0.0374]^(T), w₃ ⁽¹⁾=[0.1320, 0.9014, 0.4123]^(T), p⁽¹⁾=[0.1,0.1, 0.1]^(T)(W), α⁽¹⁾=[0.0031, 0.0109, 0.0046]^(T) and R_(sum)⁽¹⁾=0.2828 bps/Hz, the iteration stopping condition is not met, and thesecond iteration is performed.

In the second iteration, w₁ ⁽²⁾=[0.4928, −0.4172, 0.7636]^(T), w₂⁽²⁾=[0.5021, 0.3088, −0.8078]^(T), w₃ ⁽²⁾=[−0.6838, 0.5527, 0.4764]^(T),p⁽²⁾=[0.1, 0.1, 0.1]^(T)(W), α⁽²⁾=[0.2764, 0.1744, 0.395]^(T) andR_(sum) ⁽²⁾=14.4409 bps/Hz, the iteration stopping condition is not met,and the third iteration is performed.

In the third iteration, w₁ ⁽³⁾=[0.4768, −0.4224, 0.7709]^(T), w₂⁽³⁾=[0.4991, 0.2991, −0.8133]^(T), w₃ ⁽³⁾=[−0.6889, 0.5362, 0.4878]^(T),p⁽³⁾=[0.1, 0.1, 0.1]^(T) (W), α⁽³⁾=[0.2531, 0.1732, 0.4379]^(T) andR_(sum) ⁽³⁾=15.5196 bps/Hz, the iteration stopping condition is not met,and the fourth iteration is performed.

In the fourth iteration, w₁ ⁽⁴⁾=[0.4768, −0.4224, 0.7709]^(T), w₂⁽⁴⁾=[0.4991, 0.2991, −0.8133]^(T), w₃ ⁽⁴⁾=[−0.6889, 0.5362, 0.4878]^(T),p⁽⁴⁾=[0.1, 0.1, 0.1]^(T)(W), α⁽⁴⁾=[0.2531, 0.1732, 0.4379]^(T) andR_(sum) ⁽⁴⁾=15.5196 bps/Hz, the iteration stopping condition is met, theiteration process is stopped, and the following final results areoutputted:

w₁=[0.4768, −0.4224, 0.7709]^(T), w₂=[0.4991, 0.2991, −0.8133]^(T),w₃=[−0.6889, 0.5362, 0.4878]^(T) p=[0.1, 0.1, 0.1]^(T) (W), α=[0.2531,0.1732, 0.4379]^(T), R_(sum)=15.5196 bps/Hz. Using the resourcedistribution method, the rate provided by the cognitive network for theprimary user is 1 bps/Hz, which just achieves the target transmissionrate of the primary user.

The FIG. 2 and FIG. 3 are simulation curves of the invention, and thesimulation results are the average values of 10⁶ independentexperiments.

The FIG. 2 is a change curve of the transmission rate provided by thecognitive network for the primary user along with the peak transmissionpower p_(max) of the cognitive users when the target transmission rateof the primary user is R_(pk)=1 bps/Hz, R_(pk)=2 bps/Hz and R_(pk)=3bps/Hz respectively. The FIG. 2 illustrates that, the resourcedistribution method of the invention can enable the actual transmissionrate of the primary user to just achieve its required targettransmission rate regardless of the value of the target transmissionrate R_(pk).

The FIG. 3 is a change curve of the throughput of the cognitive SIMOnetwork obtained by the resource distribution method of the inventionalong with the peak transmission power p_(max) of the cognitive users,and the different target transmission rates R_(pk) and different numbersof the antennas M are considered in simulation. FIG. 3 shows that, alongwith the increase in the peak transmission power of the cognitive users,the throughput performance of the cooperative cognitive SIMO network isin a linear increase trend, namely the transmission power of thecognitive users is not limited by the primary network, so that thecooperative cognitive SIMO network can realize a large-range networkcoverage.

Compared with the prior art, the invention has the following advantages:

-   -   The invention designs the resource distribution method for        throughput maximization in the cognitive SIMO network based on        the cooperative relay. According to the combined adjustment of        the transmission power vector, the power distribution factor        vector and the beamforming weight vectors, the maximum        throughput performance of the cognitive network can be realized        while the target transmission rate of the primary user is        ensured, in addition, very fast convergence rate can be realized        in the proposed resource distribution method.

The invention claimed is:
 1. A resource distribution method forthroughput maximization in a cooperative cognitive SIMO networkcomprising: step 1: a transmitter of a primary user broadcasts acooperation request message (CRM), a receiver of the primary userreplies a cooperation acknowledge message (CAM), a cognitive basestation estimates channel state information in the cognitive SIMOnetwork from the received CRM and CAM, then judges whether the cognitiveSIMO network has ability of cooperating with the primary user to achievea target transmission rate of the primary user or not, if yes, thecognitive base station sends a cooperation confirmation message (CCM) tothe primary user and the cognitive SIMO network being accessible to afrequency band licensed to the primary user; and otherwise, thecognitive SIMO network is non-accessible to the licensed frequency band;step 2: the cognitive network receives data of the primary user,comprising: after the transmitter and the receiver of the primary userreceive the CCM, the transmitter of the primary user starts to sendinformation data, the cognitive network keeps silent and receives dataof the primary user, simultaneously, the cognitive base station and thecognitive users being capable of successfully decoding the data of theprimary user decode the data; step 3: the cognitive users send data ofthemselves and simultaneously relay the data of the primary user,comprising: the cognitive users which successfully decode the data ofthe primary user spend part of their transmission power α_(c)p_(c), c∈U₁to send the data of themselves to the cognitive base station, and theremaining transmission power (1−α_(c))p_(c), c∈U₁ is spent forforwarding the data of the primary user to the primary receiver; thecognitive users which cannot successfully decode the data do not forwardthe data of the primary user, all their transmission power p_(c), c∈U₂is used for sending the data of themselves to the cognitive basestation, wherein the set U₁ represents a set of the cognitive usersbeing capable of successfully decoding the data of the primary user, aset U₂ represents the set of the cognitive users being incapable ofsuccessfully decoding the data of the primary user, p_(c) represents atransmission power of a cth cognitive user and α_(c) represents a powerdistribution factor of the cth cognitive user; step 4: the cognitivebase station eliminates the interference caused by the data of theprimary user from the received mixed signals and performs beamformingfor the signals after eliminating the interference, according to thecombined adjustment of a transmission power vector p=[p₁, p₂, . . . ,p_(N)]^(T), a power distribution factor vector α=[α₁, α₂, . . . ,α_(N)]^(T), α_(c)=1, c∈U₂, and a beamforming weight vectorsw_(c)=[w_(c,1), w_(c,2), . . . , w_(c,M)]^(T),c=1, 2, . . . , N, amaximum throughput can be realized in the cognitive SIMO network on apremise of ensuring the target transmission rate of the primary user,wherein M represents a number of antennas configured at the cognitivebase station, and N represents a number of the cognitive users in thecognitive network.
 2. The resource distribution method for throughputmaximization in the cooperative cognitive SIMO network according toclaim 1, characterized in that the specific implementation method of thestep 4 comprising: step 4.1 initializing: n=0, p_(c) ^((n))=p_(c,max),c=1,2, . . . , N, α_(c) ^((n))=x, c=1, 2, . . . , N and R_(sum)^((n))=0, wherein n represents the number of iterations, p_(c) ^((n))and α_(c) ^((n)) represent the transmission power and the powerdistribution factor of the cth cognitive user at the nth iterationrespectively, p_(c,max) represents the peak transmission power allowedby the cth cognitive user, and R_(sum) ^((n)) represents the throughputof the cognitive network at the nth iteration; and setting a judgingcriterion of iteration stopping ε, ε∈[10⁻², 10⁻⁴]; step 4.2 adding 1 tothe number of the iterations: n=n+1; step 4.3 fixing the transmissionpower vector and the power distribution factor vector as p^((n-1)) andα^((n-1)) of the (n−1)th iteration, and enabling the cognitive basestation to utilize a maximum SINR beamforming criterion to calculate thebeamforming weight vectors w_(c) ^((n)), c=1, 2, . . . , N of the nthiteration, wherein the calculation formula is as follows:$\begin{matrix}{w_{c}^{(n)} = {{\eta\left( {{\sum\limits_{{i = 1},{i \neq c}}^{N}\;{\alpha_{i}^{({n - 1})}p_{i}^{({n - 1})}h_{i}^{cbH}h_{i}^{cb}}} + {\sigma_{b}^{2}I_{M}}} \right)}^{- 1}h_{c}^{cb}}} & (1)\end{matrix}$ wherein I_(M) represents an M×M unit matrix, η is a scalerfactor for normalizing w_(c) ^((n)), and h_(c) ^(cb), c=1, 2, . . . , Nrepresents an M-dimensional channel vector response from the cthcognitive user to the cognitive base station, h_(i) ^(cbH) is aconjugate of h_(i) ^(cb), and σ_(b) ² represents the channel noise powerreceived by the cognitive base station; step 4.4 fixing the beamformingweight vectors as w_(c) ^((n)), c=1, 2, . . . , N of the nth iteration,fixing the power distribution factor vector as α^((n-1)) of the (n−1)thiteration and calculating the transmission power vector p^((n)) of thenth iteration; step 4.5 fixing the beamforming weight vectors and thetransmission power vector as w_(c) ^((n)), c=1, 2, . . . , N and p^((n))of the nth iteration and calculating the power distribution factorvector α^((n)) of the nth iteration; step 4.6 calculating the throughputR_(sum) ^((n)) of the cognitive network after the nth iteration byutilizing the following formula: $\begin{matrix}{R_{sum}^{(n)} = {\sum\limits_{c = 1}^{N}\;{\frac{1}{2}\;{\log\left( {1 + \frac{\alpha_{c}^{(n)}p_{c}^{(n)}{{w_{c}^{{(n)}H}h_{c}^{cb}}}^{2}}{{\sum\limits_{{i = 1},{i \neq c}}^{N}\;{\alpha_{i}^{(n)}p_{i}^{(n)}{{w_{c}^{{(n)}H}h_{i}^{cb}}}^{2}}} + \sigma_{b}^{2}}} \right)}}}} & (2)\end{matrix}$ step 4.7 judging whether the iteration stopping condition|R_(sum) ^((n))−R_(sum) ^((n-1))|/R_(sum) ^((n-1))≦ε is met or not, ifso, indicating that the throughputs of the cognitive network, which areobtained in the nth iteration and the (n−1)th iteration hardly change,namely the iteration process converges, then continuously implementingthe step 4.8; and otherwise, repeatedly implementing the step 4.2; step4.8 outputting the final values after convergence: w_(c)=w_(c) ^((n)),c=1, 2, . . . , N, p=p^((n)), α=α^((n)) and R_(sum)=R_(sum) ^((n)),wherein the throughput R_(sum) of the cognitive network is maximum atthis time.
 3. The resource distribution method for throughputmaximization in the cooperative cognitive SIMO network according toclaim 2, characterized in that the calculation process of thetransmission power vector p^((n)) in the step 4.4 at the nth iterationis as follows: 1) initializing: m=0 and {circumflex over (p)}_(c)^((m))=0, c=1, 2, . . . , N, wherein m represents the number ofiterations, {circumflex over (p)}_(c) ^((m)) represents the transmissionpower of the cth cognitive user at the mth iteration, and setting thejudging criteria of iteration stopping ε, ε∈[10⁻², 10⁻⁴]; 2) adding 1 tothe number of iterations: m=m+1; 3) calculating the transmission power{circumflex over (p)}_(c) ^((m)), c=1, 2, . . . , N of the cth cognitiveuser at the mth iteration by utilizing the following formula;$\begin{matrix}{{\overset{\mu}{p}}_{c}^{(m)} = \left\lbrack \left( {\sum\limits_{{k = 1},{k \neq c}}^{N}\;\frac{\alpha_{c}^{({n - 1})}{{w_{k}^{{(n)}H}h_{c}^{cb}}}^{2}}{{\sum\limits_{{i = 1},{i \neq k}}^{N}\;{{\overset{\mu}{p}}_{i}^{({m - 1})}\alpha_{i}^{({n - 1})}{{w_{k}^{{(n)}H}h_{c}^{cb}}}^{2}}} + \sigma_{b}^{2}}} \right)^{- 1} \right\rbrack^{p_{c,\max}}} & (3)\end{matrix}$ wherein [b]^(p) ^(c,max) takes a minimum value of b andp_(c,max); 4) judging whether the iteration stopping condition∥{circumflex over (p)}^((m))−{circumflex over (p)}^((m-1))∥/∥{circumflexover (p)}^((m-1))∥<ε is met or not, if so, indicating that the powervectors obtained in the nth iteration and the (n−1)th iteration hardlychange, namely the iteration process converges, then continuouslyimplementing the step 5); and otherwise, repeatedly implementing thestep 2); 5) outputting the final convergent value: p^((n))={circumflexover (p)}^((m)).
 4. The resource distribution method for throughputmaximization in the cooperative cognitive SIMO network according toclaim 2, characterized in that the calculation process of the powerdistribution factor vector α^((n)) in the step 4.5 at the nth iterationcomprising: 1) initializing: m=0, {circumflex over (α)}_(c)^((m))=0(c∈U₁) and {circumflex over (α)}_(c) ^((m))=1(c∈U₂), wherein mrepresents the number of iterations, {circumflex over (α)}_(c) ^((m))represents the power distribution factor of the cth cognitive user atthe mth iteration; and setting the judging criteria of iterationstopping ε, ε∈[10⁻², 10⁻⁴]; 2) adding 1 to the number of iterations:m=m+1; 3) calculating the power distribution factor of the c(c∈U₁) thcognitive user at the mth iteration by utilizing the following formula:$\begin{matrix}{{\overset{\mu}{\alpha}}_{c}^{(m)} = \left\lbrack {{{\left( {{\sum\limits_{{k \in U_{1}},{k \neq c}}\;\frac{p_{c}^{(n)}{{w_{k}^{{(n)}H}h_{c}^{cb}}}^{2}}{{\sum\limits_{{i \in U_{1}},{i \neq k}}\;{{\overset{\mu}{\alpha}}_{i}^{({m - 1})}p_{i}^{(n)}{{w_{k}^{{(n)}H}h_{i}^{cb}}}^{2}}} + q_{k}^{1}}} + \left. \quad\mspace{11mu}\mspace{245mu}{{\sum\limits_{k \in U_{2}}\;\frac{p_{c}^{(n)}{{w_{k}^{{(n)}H}h_{c}^{cb}}}^{2}}{{\sum\limits_{i \in U_{1}}\mspace{11mu}{\alpha_{i}^{({m - 1})}{\overset{\mu}{p}}_{i}{{w_{k}^{{(n)}H}h_{i}^{cb}}}^{2}}} + q_{k}^{2}}} + {{\lambda g}_{c}\left( p_{c}^{(n)} \right)}} \right)^{- 1}} \right\rbrack^{1}\mspace{85mu}{wherein}\mspace{14mu} q_{k}^{1}} = {{\sum\limits_{i \in U_{2}}\;{p_{i}^{(n)}{{w_{k}^{{(n)}H}h_{i}^{cb}}}^{2}}} + {\sigma_{b}^{2}\left( {k \in U_{1}} \right)}}},\mspace{79mu}{{{q_{k}^{2}{\underset{\mspace{11mu}{{i \in U_{2}},{i \neq k}}}{= \sum}\;{p_{i}^{(n)}{{w_{k}^{{(n)}H}h_{i}^{cb}}}^{2}}}} + {{\sigma_{b}^{2}\left( {k \in U_{2}} \right)}\mspace{14mu}{and}\mspace{11mu}\mspace{85mu}{g_{c}\left( p_{c}^{(n)} \right)}}} = {\left\lbrack {1 + {1/\left( {2^{2\; R_{pk}} - 1} \right)}} \right\rbrack p_{c}^{(n)}{h_{c}^{cp}}^{2}}},\lambda} \right.} & (4)\end{matrix}$ represents an any auxiliary Lagrange factor and h_(c)^(cp) represents the channel response from the cth cognitive user to thereceiver of the primary user, wherein for the cognitive users in the setU₂, the power distribution factor {circumflex over (α)}_(c)^((m))=1(c∈U₂); 4) judging whether the iteration stopping condition∥{circumflex over (α)}^((m))−{circumflex over (α)}^((m-1))∥/∥{circumflexover (α)}^((m-1))∥<ε is met or not, if so, indicating that the powerdistribution factor vectors obtained in the nth iteration and the(n−1)th iteration hardly change, namely the iteration process converges,then continuously implementing the step 5); and otherwise, repeatedlyimplementing the step 2); 5) outputting the final convergent value:α(λ)={circumflex over (α)}^((m)).
 5. The resource distribution methodfor throughput maximization in the cooperative cognitive SIMO networkaccording to claim 4, characterized in that the value of the auxiliaryLagrange factor λ is determined according to a bisection search methodwhen the power distribution factor vector α^((n)) is calculated, and thespecific implementation process comprising: 1) setting initial valuesλ⁺=0 and λ⁻ of a bisection search algorithm, wherein λ⁺ is the initialvalue for realizing${{\sum\limits_{c \in U_{1}}{{{\overset{\_}{\alpha}}_{c}\left( \lambda^{+} \right)}{g_{c}\left( p_{c}^{(n)} \right)}}} > {C\left( p^{(n)} \right)}},$indicating that λ⁺ can enable the cognitive network to provide a greatertransmission rate than the target transmission rate of the primary user;and λ⁻ is the initial value for realizing${{\sum\limits_{c \in U_{1}}{{{\overset{\_}{\alpha}}_{c}\left( \lambda^{+} \right)}{g_{c}\left( p_{c}^{(n)} \right)}}} < {C\left( p^{(n)} \right)}},$indicating that λ⁻ can enable the cognitive network to provide a lesstransmission rate than the target transmission rate of the primary user,wherein${{C\left( p^{(n)} \right)} = {{\frac{1}{2^{2R_{pk}} - 1}{\sum\limits_{c \in N_{1}}{p_{c}^{(n)}{h_{c}^{cp}}^{2}}}} - {\sum\limits_{c \in N_{2}}{p_{c}^{(n)}{h_{c}^{cp}}^{2}}} - \sigma_{p}^{2}}},$is the channel noise power received by the receiver of the primary user,and R_(pk) is the target transmission rate of the primary user; Settingthe judging criteria of iteration stopping ε, ε∈[10⁻², 10⁻⁴]; 2)enabling ${\lambda = \frac{\lambda^{+} + \lambda^{-}}{2}},$ andobtaining the power distribution factor vector α(λ) according to thecalculation process of the power distribution factor in claim 4;${{{If}\mspace{14mu}{\sum\limits_{c \in N_{1}}{{{\overset{\_}{\alpha}}_{c}(\lambda)}{g_{c}\left( p_{c}^{(n)} \right)}}}} < {C\left( p^{(n)} \right)}},{{{{then}\mspace{14mu}\lambda^{-}} = \lambda};}$${{{If}\mspace{14mu}{\sum\limits_{c \in N_{1}}{{{\overset{\_}{\alpha}}_{c}(\lambda)}{g_{c}\left( p_{c}^{(n)} \right)}}}} > {C\left( p^{(n)} \right)}},{{{{then}\mspace{14mu}\lambda^{+}} = \lambda};}$3) judging whether the search stopping condition${{{{\sum\limits_{c \in U_{1}}{{{\overset{\_}{\alpha}}_{c}(\lambda)}{g_{c}\left( p_{c}^{(n)} \right)}}} - {C\left( p^{(n)} \right)}}}/{C\left( p^{(n)} \right)}} \leq ɛ$is met or not, if so, indicating that the transmission rate which isprovided by the cognitive network for the primary user is basicallyequivalent to the target transmission rate of the primary user, thencontinuously implementing the step 4); and otherwise, repeatedlyimplementing the step 2); 4) outputting the final result: α^((n))= α(λ).6. The resource distribution method for throughput maximization in thecooperative cognitive SIMO network according to claim 2, characterizedin that, in the step 4.1, α_(c) ^((n))=x, c=1, 2, . . . , N, wherein xis a decimal and x∈[0,1].